prediction_challenge

prediction_challenge - fixed length) Predictors Each...

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Challenges of Predicting Mobile User Patterns Jeeyoung Kim Ahmed Helmy
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What are the Challenges? What is a “success”? Definition (hit or miss? Or gray area allowed?) Granularity (aggregation, different levels of Granularity (aggregation, different levels of prediction…) How close to “ground truth”? Characteristic of the data Never really can compare
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Predictors used. . Order k Markov Chain Predictor Lempel-Ziv Predictor Regression, other data mining techniques…
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Predictors: Order-k Markov Assumes location can be predicted from the current context which is the sequence of the k most recent symbols in the location history Markov model represents each state as a context, and transitions represent the possible locations that follow that context. We use Order 1, 2 and 3 predictors for our prediction
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Predictors: Lempel-Ziv (LZ) Predicts in the case when the next symbol in the produced sequence is dependent on only its current state (but does not have to correspond to a string of
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Unformatted text preview: fixed length) Predictors Each predictor is run for each of the data sets The prediction accuracy is measured as the percentage of correct predictions of the next AP to visit, next building to visit (different granularities of prediction) AP level prediction results can be aggregated into building levels (1 out of 5 cells hand-off prediction) Markov Prediction Accuracies for each years trace Comparing the Markov order 1,2 and 3 predictors for each 1 year period (01-02, 03-04, 05-06) Time evolution of predictability Time evolution over time for the 3 years of each Markov order 1,2 and 3 predictors Aggregated prediction results Showing the drastic difference of AP level success and Building level success (01-02, 02-03, VoIP) Conclusions Same predictors can differ in results regarding the granularity or the definition of success Due to the nature of the data itself, it is very difficult to compare it to the ground truth...
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This note was uploaded on 05/27/2011 for the course CIS 4930 taught by Professor Staff during the Spring '08 term at University of Florida.

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prediction_challenge - fixed length) Predictors Each...

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